The scarcity of labeled training data across many languages is a significant roadblock for multilingual neural language processing. We approach the lack of in-language training data using sentence embeddings that map text written in different languages, but with similar meanings, to nearby embedding space representations. The representations are produced using a dual-encoder based model trained to maximize the representational similarity between sentence pairs drawn from parallel data. The representations are enhanced using multitask training and unsupervised monolingual corpora. The effectiveness of our multilingual sentence embeddings are assessed on a comprehensive collection of monolingual, cross-lingual, and zeroshot/few-shot learning tasks.1 Models based on this work are available at https: //tfhub.dev/ as: universal-sentence-encoder-xling/ende, universal-sentence-encoder-xling/en-fr, and universalsentence-encoder-xling/en-es. A large multilingual model is available as universal-sentence-encoder-xling/many.
Nowadays there is an increasing trend in the usage of computers for storing documents. As a result of it substantial volume of data is stored in the computers in the form of documents. The documents can be of any form such as structured documents, semi-structured documents and unstructured documents. Retrieving useful information from huge volume of documents is very tedious task. Text mining is an inspiring research area as it tries to discover knowledge from unstructured text. This paper gives an overview of concepts, applications, issues and tools used for text mining.
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